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Autor(en): 
  • Jan G. de Gooijer
  • Elements of Nonlinear Time Series Analysis and Forecasting 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  April 2017  
    Genre:  Schulbücher 
     
    Applications of Nonlinear Dynamics and Chaos Theory / B / Dynamics & statics / Economics, finance, business & management / Epidemiology & medical statistics / Mathematics and Statistics / Nonlinear Optics / Nonlinear science / Probability & statistics / Statistical physics / Statistical Theory and Methods / Statistics / Statistics for Life Sciences, Medicine, Health Sciences / Statistics in Life Sciences, Medicine, Health Sciences
    ISBN:  9783319432519 
    EAN-Code: 
    9783319432519 
    Verlag:  Springer Nature EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Springer Series in Statistics  
    Dimensionen:  H 254 mm / B 178 mm / D  
    Gewicht:  13317 gr 
    Seiten:  618 
    Illustration:  XXI, 618 p. 95 illus. in color., farbige Illustrationen 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a "theorem-proof" format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods.

    The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.

    To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

     


      



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